检索结果-内蒙古大学图书馆

Roadmap for Optical Tweezers

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Volpe, Giovanni Maragò, Onofrio M. Rubinzstein-Dunlop, Halina Pesce, Giuseppe Stilgoe, Alexander B. Volpe, Giorgio Tkachenko, Georgiy Truong, Viet Giang Chormaic, Síle Nic Kalantarifard, Fatemeh Elahi, Parviz Käll, Mikael Callegari, Agnese Marqués, Manuel I. Neves, Antonio A.R. Moreira, Wendel L. Fontes, Adriana Cesar, Carlos L. Saija, Rosalba Saidi, Abir Beck, Paul Eismann, Jörg S. Banzer, Peter Fernandes, Thales F.D. Pedaci, Francesco Bowen, Warwick P. Vaippully, Rahul Lokesh, Muruga Roy, Basudev Thalhammer, Gregor Ritsch-Marte, Monika García, Laura Pérez Arzola, Alejandro V. Castillo, Isaac Pérez Argun, Aykut Muenker, Till M. Vos, Bart E. Betz, Timo Cristiani, Ilaria Minzioni, Paolo Reece, Peter J. Wang, Fan McGloin, David Ndukaife, Justus C. Quidant, Romain Roberts, Reece P. Laplane, Cyril Volz, Thomas Gordon, Reuven Hanstorp, Dag Marmolejo, Javier Tello Bruce, Graham D. Dholakia, Kishan Li, Tongcang Brzobohatý, Oto Simpson, Stephen H. Zemánek, Pavel Ritort, Felix Roichman, Yael Bobkova, Valeriia Wittkowski, Raphael Denz, Cornelia Kumar, G.V. Pavan Foti, Antonino Donato, Maria Grazia Gucciardi, Pietro G. Gardini, L. Bianchi, G. Kashchuk, A. Capitanio, M. Paterson, Lynn Jones, P.H. Berg-Sørensen, Kirstine Barooji, Younes F. Oddershede, Lene B. Pouladian, Pegah Preece, Daryl Adiels, Caroline Beck De Luca, Anna Chiara Magazzù, A. Ciriza, D. Bronte Iatì, M.A. Swartzlander, Grover A. Department of Physics University of Gothenburg Gothenburg41296 Sweden CNR IPCF Istituto per i Processi Chimico-Fisici Messina Italy ARC CoE for Engineered Quantum Systems School of Mathematics and Physics The University of Queensland Australia Department of Chemistry University College London 20 Gordon Street LondonWC1H 0AJ United Kingdom Dipartimento di Fisica Università degli studi di Napoli Federico II Italy Okinawa Institute of Science and Technology Graduate University Japan Department of Health Technology Technical University of Denmark Lyngby Denmark Physics Department Boğaziçi University Istanbul Turkey Chalmers University of Technology Gothenburg Sweden Departamento de Física de Materiales IFIMAC Instituto Nicolás Cabrera Universidad Autónoma de Madrid Spain Santo André São Paulo Brazil Tratamento de dados geofísicos Tecnologia e processamento de geologia e geofísica Exploração Petrobras - Petróleo Brasileiro S.A. Rio de Janeiro Rio de Janeiro Brazil Pernambuco Recife Brazil Ceará Fortaleza Brazil Campinas São Paulo Brazil Dipartimento di Scienze Matematiche e Informatiche Scienze Fisiche e Scienze della Terra Università di Messina Italy Institute of Optics Information and Photonics University Erlangen-Nuremberg Staudtstr. 7/B2 ErlangenD-91058 Germany Max Planck Institute for the Science of Light Staudtstr. 2 ErlangenD-91058 Germany Institute of Physics University of Graz NAWI Graz Universitätsplatz 5 Graz8010 Austria Centre de Biologie Structurale CNRS INSERM Univ.Montpellier France Indian Institute of Technology Madras India Institute for Biomedical Physics Medical University of Innsbruck Austria Instituto de Física Universidad Nacional Autónoma de México Ciudad de MéxicoC. P. 04510 Mexico Departamento de Física Universidad Autónoma Metropolitana-Iztapalapa San Rafael Atlixco 186 Ciudad de México09340 Mexico Third Institute of Physics - Biophysics Georg August University Göttingen Germany Department of Electrical

Optical tweezers are tools made of light that enable contactless pushing, trapping, and manipulation of objects ranging from atoms to space light sails. Since the pioneering work by Arthur Ashkin in the 1970s, optical tweezers have evolved into sophisticated instruments and have been employed in a broad range of applications in life sciences, physics, and engineering. These include accurate force and torque measurement at the femtonewton level, microrheology of complex fluids, single micro- and nanoparticle spectroscopy, single-cell analysis, and statistical-physics experiments. This roadmap provides insights into current investigations involving optical forces and optical tweezers from their theoretical foundations to designs and setups. It also offers perspectives for applications to a wide range of research fields, from biophysics to space exploration. Copyright © 2022, The Authors. All rights reserved.

关键词： Optical tweezers

AI-skin: Skin disease recognition based on self-learning and wide data collection through a closed loop framework

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Chen, Min Zhou, Ping Wu, Di Hu, Long Hassan, Mohammad Mehedi Alamri, Atif School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Data and Computer Science Sun Yat-sen University Guangzhou China Guangdong Province Key Laboratory of Big Data Analysis and Processing Guangzhou China Chair of Pervasive and Mobile Computing Information Systems Department College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia Chair of Pervasive and Mobile Computing Software Engineering Department CCIS King Saud University Riyadh11543 Saudi Arabia

There are a lot of hidden dangers in the change of human skin conditions, such as the sunburn caused by long-time exposure to ultraviolet radiation, which not only has aesthetic impact causing psychological depression and lack of selfconfidence, but also may even be life-threatening due to skin canceration. Current skin disease researches adopt the auto-classification system for improving the accuracy rate of skin disease classification. However, the excessive dependence on the image sample database is unable to provide individualized diagnosis service for different population groups. To overcome this problem, a medical AI framework based on data width evolution and self-learning is put forward in this paper to provide skin disease medical service meeting the requirement of real time, extendibility and individualization. First, the wide collection of data in the close-loop information flow of user and remote medical data center is discussed. Next, a data set filter algorithm based on information entropy is given, to lighten the load of edge node and meanwhile improve the learning ability of remote cloud analysis model. In addition, the framework provides an external algorithm load module, which can be compatible with the application requirements according to the model selected. Three kinds of deep learning model, i.e. LeNet-5, AlexNet and VGG16, are loaded and compared, which have verified the universality of the algorithm load module. The experiment platform for the proposed real-time, individualized and extensible skin disease recognition system is built. And the system's computation and communication delay under the interaction scenario between tester and remote data center are analyzed. It is demonstrated that the system we put forward is reliable and effective. Copyright © 2019, The Authors. All rights reserved.

关键词： Data acquisition

The molecular epidemiology of multiple zoonotic origins of SARS-CoV-2

学校读者我要写书评

暂无评论

四川生理科学杂志 2022年第7期44卷 1299-1299页

作者： Jonathan E Pekar Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA 92093 USA Department of Biomedical Informatics University of California San Diego La Jolla CA 92093 USA Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los Angeles CA 90095 USA Department of Immunology and Microbiology The Scripps Research Institute La Jolla CA 92037 USA Department of Computer Science and Engineering University of California San Diego La Jolla CA 92093 USA Department of Mathematics University of California San Diego La Jolla CA 92093 USA Department of Ecology and Evolutionary Biology University of Arizona Tucson AZ 85721 USA W. Harry Feinstone Department of Molecular Microbiology and Immunology Johns Hopkins Bloomberg School of Public Health Baltimore MD 21205 USA New York City Public Health Laboratory New York City Department of Health and Mental Hygiene New York NY 11101 USA Department of Microbiology Institute for Viral Diseases Biosafety Center College of Medicine Korea University Seoul South Korea BK21 Graduate Program Department of Biomedical Sciences Korea University College of Medicine Seoul 02841 Republic of Korea National Public Health Laboratory National Centre for Infectious Diseases Singapore Malaysia Genome and Vaccine Institute Jalan Bangi 43000 Kajang Selangor Malaysia Department of Medicine University of California San Diego La Jolla CA 92093 USA Department of Microbiology and Immunology School of Medicine Tulane University New Orleans LA 70112 USA Zalgen Labs Frederick MD 21703 USA Global Virus Network (GVN) Baltimore MD 21201 USA Sydney Institute for Infectious Diseases School of Life and Environmental Sciences and School of Medical Sciences The University of Sydney Sydney NSW 2006 Australia Institute of Evolutionary Biology University of Edinburgh King’s Buildings Edinburgh EH9 3FL UK Department of Biomathematics David Geffen School of Medicine Un

Understanding the circumstances that lead to pandemics is important for their prevention. Here, we analyze the genomic diversity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) early in the coronavirus disease 2019 (COVID-19) pandemic. We show that SARS-CoV-2 genomic diversity before February 2020 likely comprised only two distinct viral lineages, denoted A and B. Phylodynamic rooting methods, coupled with epidemic simulations, reveal that these lineages were the result of at least two separate cross-species transmission events into humans. The first zoonotic transmission likely involved lineage B viruses around 18 November 2019 (23 October-8 December), while the separate introduction of lineage A likely occurred within weeks of this event.

关键词： epidemiology likely acute

学校读者我要写书评

暂无评论

Preface

CEUR Workshop Proceedings 2021年 2899卷

作者： Kovalev, Igor Semenkin, Eugene Gulyaev, Yuri Barakaev, Nusratilla Shaidurov, Vladimir Škraba, Andrej Tsarev, Roman Legalov, Alexander Saramud, Mikhail Grosspietsch, Karl-Erwin Stupina, Alena Department of Computer Science Siberian Federal University Krasnoyarsk Russia Department of system analysis and operations research of Reshetnev University Krasnoyarsk Russia International and Russian Union of Scientific and Engineering Associations Moscow Russia Rector of Bukhara Engineering Technological Institute Bukhara Uzbekistan Institute of Computational Modeling SB RAS Krasnoyarsk Russia University of Maribor UM Faculty of Organizational Sciences - Cybernetics and Decision Support Systems Laboratory Slovenia Department of Informatics Siberian Federal University Krasnoyarsk Russia Faculty of Computer Science School of Software Engineering HSE University Moscow Russia Department of Informatics Siberian Federal University Krasnoyarsk Russia European Organization for Information and Microelectronics Germany The Siberian Federal University Krasnoyarsk Russia

Ensemble of Learning Project Productivity in software Effort Based on Use Case Points

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Azzeh, Mohammad Nassif, Ali Bou Banitaan, Shadi López-Martín, Cuauhtémoc Department of Software Engineering Applied Science Private University Amman Jordan Department of Electrical and Computer Engineering University of Sharjah Sharjah United Arab Emirates Department of Mathematics Computer Science and Software Engineering University of Detroit Mercy United States Department of Information Systems Universidad de Guadalajara Mexico

It is well recognized that the project productivity is a key driver in estimating software project effort from Use Case Point size metric at early software development stages. Although, there are few proposed models for predicting productivity, there is no consistent conclusion regarding which model is the superior. Therefore, instead of building a new productivity prediction model, this paper presents a new ensemble construction mechanism applied for software project productivity prediction. Ensemble is an effective technique when performance of base models is poor. We proposed a weighted mean method to aggregate predicted productivities based on average of errors produced by training model. The obtained results show that the using ensemble is a good alternative approach when accuracies of base models are not consistently accurate over different datasets, and when models behave diversely. Copyright © 2018, The Authors. All rights reserved.

关键词： Productivity

Multi-task variational information bottleneck

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Qian, Weizhu Chen, Bowei Zhang, Yichao Wen, Guanghui Gechter, Franck Le laboratoire Connaissance et Intelligence Artificielle Distribuées Université Bourgogne Franche-Comté France Adam Smith Business School University of Glasgow United Kingdom Department of Computer Science and Technology Tongji University China Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education Shanghai200092 China Department of Systems Science School of Mathematics Southeast University Nanjing210016 China School of Engineering RMIT University MelbourneVIC3000 Australia Le laboratoire Connaissance et Intelligence Artificielle Distribuées Université Bourgogne Franche-Comté and Mosel LORIA UMR CNRS Université de Lorraine 7503 France

Multi-task learning (MTL) is an important subject in machine learning and artificial intelligence. Its applications to computer vision, signal processing, and speech recognition are ubiquitous. Although this subject has attracted considerable attention recently, the performance and robustness of the existing models to different tasks have not been well balanced. This article proposes an MTL model based on the architecture of the variational information bottleneck (VIB), which can provide a more effective latent representation of the input features for the downstream tasks. Extensive observations on three public data sets under adversarial attacks show that the proposed model is competitive to the state-of-the-art algorithms concerning the prediction accuracy. Experimental results suggest that combining the VIB and the task-dependent uncertainties is a very effective way to abstract valid information from the input features for accomplishing multiple tasks. © 2020, CC BY.

关键词： Deep learning

Input optics systems of the KAGRA detector during O3GK (vol 2023, 023F01, 2023)

学校读者我要写书评

暂无评论

PROGRESS OF THEORETICAL AND EXPERIMENTAL PHYSICS 2023年第5期2023卷

作者： Akutsu, T. Ando, M. Arai, K. Arai, Y. Araki, S. Araya, A. Aritomi, N. Asada, H. Aso, Y. Bae, S. Bae, Y. Baiotti, L. Bajpai, R. Barton, M. A. Cannon, K. Cao, Z. Capocasa, E. Chan, M. Chen, C. Chen, K. Chen, Y. Chiang, C-I Chu, H. Chu, Y-K Eguchi, S. Enomoto, Y. Flaminio, R. Fujii, Y. Fujikawa, Y. Fukunaga, M. Fukushima, M. Furuhata, T. Gao, D. Ge, G-G Ha, S. Hagiwara, A. Haino, S. Han, W-B Hasegawa, K. Hattori, K. Hayakawa, H. Hayama, K. Himemoto, Y. Hiranuma, Y. Hirata, N. Hirose, E. Hong, Z. Hsieh, B-H Huang, G-Z Huang, H-Y Huang, P. Huang, Y-C Huang, Y-J Hui, D. C. Y. Ide, S. Ikenoue, B. Imam, S. Inayoshi, K. Inoue, Y. Ioka, K. Ito, K. Itoh, Y. Izumi, K. Jeon, C. Jin, H-B Jung, K. Jung, P. Kaihotsu, K. Kajita, T. Kakizaki, M. Kamiizumi, M. Kanbara, S. Kanda, N. Kang, G. Kataoka, Y. Kawaguchi, K. Kawai, N. Kawasaki, T. Kim, C. Kim, J. Kim, J. C. Kim, Ws Kim, Y-M Kimura, N. Kita, N. Kitazawa, H. Kojima, Y. Kokeyama, K. Komori, K. Kong, A. K. H. Kotake, K. Kozakai, C. Kozu, R. Kumar, R. Kume, J. Kuo, C. Kuo, H-S Kuromiya, Y. Kuroyanagi, S. Kusayanagi, K. Kwak, K. Lee, H. K. Lee, H. W. Lee, R. Leonardi, M. Li, K. L. Lin, L. C-C Lin, C-Y Lin, F-K Lin, F-L Lin, H. L. Liu, G. C. Luo, L-W Majorana, E. Marchio, M. Michimura, Y. Mio, N. Miyakawa, O. Miyamoto, A. Miyazaki, Y. Miyo, K. Miyoki, S. Mori, Y. Morisaki, S. Moriwaki, Y. Nagano, K. Nagano, S. Nakamura, K. Nakano, H. Nakano, M. Nakashima, R. Nakayama, Y. Narikawa, T. Naticchioni, L. Negishi, R. Quynh, L. Nguyen Ni, W-T Nishizawa, A. Nozaki, S. Obuchi, Y. Ogaki, W. Oh, J. J. Oh, S. H. Ohashi, M. Ohishi, N. Ohkawa, M. Ohta, H. Okutani, Y. Okutomi, K. Oohara, K. Ooi, C. Oshino, S. Otabe, S. Pan, K-C Pang, H. Parisi, A. Park, J. Arellano, F. E. Pena Pinto, I. Sago, N. Saito, S. Saito, Y. Sakai, K. Sakai, Y. Sakuno, Y. Sato, S. Sato, T. Sawada, T. Sekiguchi, T. Sekiguchi, Y. Shao, L. Shibagaki, S. Shimizu, R. Shimoda, T. Shimode, K. Shinkai, H. Shishido, T. Shoda, A. Somiya, K. Son, E. J. Sotani, H. Sugimoto, R. Suresh, J. Suzuki, T. Suzuki, T. Tagoshi, H. Takahashi, H Gravitational Wave Science Project National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Department of Physics The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Research Center for the Early Universe The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Institute for Cosmic Ray Research KAGRA Observatory The University of Tokyo 5-1-5 Kashiwa-no-Ha Kashiwa City Chiba 277-8582 Japan Accelerator Laboratory High Energy Accelerator Research Organization (KEK) 1-1 Oho Tsukuba City Ibaraki 305-0801 Japan Earthquake Research Institute The University of Tokyo 1-1-1 Yayoi Bunkyo-ku Tokyo 113-0032 Japan Department of Mathematics and Physics Graduate School of Science and Technology Hirosaki University 3 Bunkyo-cho Hirosaki Aomori 036-8561 Japan Kamioka Branch National Astronomical Observatory of Japan 238 Higashi-Mozumi Kamioka-cho Hida City Gifu 506-1205 Japan The Graduate University for Advanced Studies (SOKENDAI) 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Korea Institute of Science and Technology Information 245 Daehak-ro Yuseong-gu Daejeon 34141 Republic of Korea National Institute for Mathematical Sciences 70 Yuseong-daero 1689 Beon-gil Yuseong-gu Daejeon 34047 Republic of Korea International College Osaka University 1-1 Machikaneyama-cho Toyonaka City Osaka 560-0043 Japan School of High Energy Accelerator Science The Graduate University for Advanced Studies (SOKENDAI) 1-1 Oho Tsukuba City Ibaraki 305-0801 Japan Department of Astronomy Beijing Normal University Xinjiekouwai Street 19 Haidian District Beijing 100875 China Department of Applied Physics Fukuoka University 8-19-1 Nanakuma Jonan Fukuoka City Fukuoka 814-0180 Japan Department of Physics Tamkang University No. 151 Yingzhuan Road Danshui Dist. New Taipei City 25137 Taiwan Department of Physics

KAGRA, the underground and cryogenic gravitational-wave detector, was operated for its solo observation from February 25 to March 10, 2020, and its first joint observation with the GEO 600 detector from April 7 to April 21, 2020 (O3GK). This study presents an overview of the input optics systems of the KAGRA detector, which consist of various optical systems, such as a laser source, its intensity and frequency stabilization systems, modulators, a Faraday isolator, mode-matching telescopes, and a high-power beam dump. These optics were successfully delivered to the KAGRA interferometer and operated stably during the observations. The laser frequency noise was observed to limit the detector sensitivity above a few kilohertz, whereas the laser intensity did not significantly limit the detector sensitivity.

关键词：

Nikkei Stock Market Price Index Prediction Using Machine Learning

学校读者我要写书评

暂无评论

Journal of Physics: Conference Series 2020年第1期1566卷

作者： Lukman A Harahap Rajalida Lipikorn Asanobu Kitamoto Department of Agricultural Engineering Faculty of Agriculture University of Sumatera Utara Medan Indonesia Machine Intelligence and Multimedia Information Technology Laboratory Department of Mathematics and Computer Science Faculty of Science Chulalongkorn University Bangkok Thailand Research Organization of Information and Systems Department of Informatics National Institute of Informatics Tokyo Japan

Stock market prediction has always been a difficult process, most of the prediction rely solely on the data of the corresponding stock market. Relationship of gold and oil price with stock market performance has been proven significant in some major world stock index. Prediction of stock market price index using machine learning methods is expected to perform well, with the ability of machine learning method to predict using nonlinear inputs. The methods were commonly able to predict relatively well in predicting the values of Japanese Nikkei 225 and Japanese Nikkei 400 indexes.

关键词：

Learning from learning machines: A new generation of AI technology to meet the needs of science

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Pion-Tonachini, Luca Bouchard, Kristofer Martin, Hector Garcia Peisert, Sean Holtz, W. Bradley Aswani, Anil Dwivedi, Dipankar Wainwright, Haruko Pilania, Ghanshyam Nachman, Benjamin Marrone, Babetta L. Falco, Nicola Prabhat Arnold, Daniel Wolf-Yadlin, Alejandro Powers, Sarah Climer, Sharlee Jackson, Quinn Carlson, Ty Sohn, Michael Zwart, Petrus Kumar, Neeraj Justice, Amy Tomlin, Claire Jacobson, Daniel Micklem, Gos Gkoutos, Georgios V. Bickel, Peter J. Cazier, Jean-Baptiste Müller, Juliane Webb-Robertson, Bobbie-Jo Stevens, Rick Anderson, Mark Kreutz-Delgado, Ken Mahoney, Michael W. Brown, James B. Pattern Computer Inc. Friday HarborWA98250 United States Biosciences Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Computational Research Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Helen Wils Neuroscience Institute Redwood Center for Theoretical Neuroscience Uc Berkeley BerkeleyCA94720 United States Doe Agile BioFoundry Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Joint BioEnergy Institute Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Bcam Basque Center for Applied Mathematics Bilbao48009 Spain Computer Science University of California Davis DavisCA95616 United States Cenic La MiradaCA90638 United States Health Informatics University of California Davis School of Medicine SacramentoCA95817 United States Berkeley Institute for Data Science University of California Berkeley BerkeleyCA94720 United States Industrial Engineering and Operations Research University of California Berkeley BerkeleyCA94720 United States Environmental & Earth Sciences Area Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Nuclear Engineering University of California Berkeley BerkeleyCA94720 United States Materials Science and Technology Division Los Alamos National Laboratory Los AlamosNM87545 United States Physics Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Bioscience Division Los Alamos National Laboratory Los AlamosNM87545 United States Earth and Environmental Sciences Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Energy Technologies Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Computing and Computational Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Industrial & Systems Engineering The University of Tennessee KnoxvilleTN37996 United States Department of Computer Science University of Missouri-Saint Louis St. LouisMO63121 United

We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science create tension between identifying patterns in data versus discovering patterns in the world from data. If we address the fundamental challenges associated with "bridging the gap" between domain-driven scientific models and data-driven AI learning machines, then we expect that these AI models can transform hypothesis generation, scientific discovery, and the scientific process itself. © 2021, CC BY.

关键词： Learning systems